More ggplot2
library(tidyverse)
library(knitr)
library(broom)
library(forcats)
library(stringr)
library(ggrepel)
library(socviz)
options(digits = 3)
set.seed(1234)
theme_set(theme_minimal())Group data and the group aesthetic
Lets begin by examining the Gapminder dataset. We want to plot the life expectancy over time for each country in the data. We would map year to x and lifeExp to y.
library(gapminder)
ggplot(data = gapminder,
mapping = aes(x = year,
y = lifeExp)) +
geom_line()
Oops. This is problematic. ggplot() did not understand the actual structure of the data. We want one line drawn for every country; instead it drew a single line connecting all the dots. In this case, we should use the group aesthetic to tell ggplot() explicitly about the country-level structure of the data.
ggplot(data = gapminder,
mapping = aes(x = year,
y = lifeExp,
group = country)) +
geom_line()
The group aesthetic is only necessary if the grouping information is not already contained in the built-in variables being mapped. For instance, if we plot points to continent the grouping is already clear.
ggplot(data = gapminder,
mapping = aes(x = lifeExp,
y = gdpPercap,
color = continent)) +
geom_point()
ggplot(data = gapminder,
mapping = aes(x = lifeExp,
y = gdpPercap,
color = continent,
# redundant
group = continent)) +
geom_point()
Statistical transformations and temporary variables
As we’ve heard before, ggplot() can statistically transform and summarize the underlying data set through the use of stat_ functions. For example, to add a smoothing line to a plot, we can use geom_smooth():
ggplot(data = gapminder,
mapping = aes(x = year,
y = lifeExp)) +
geom_line(aes(group = country), alpha = .1) +
geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
geom_smooth() can generate trend lines using a variety of algorithms: LOESS, OLS, GAM, etc. To identify the specific method we use the method argument:
ggplot(data = gapminder,
mapping = aes(x = year,
y = lifeExp)) +
geom_line(aes(group = country), alpha = .1) +
geom_smooth(method = "lm")
Every geom_ function uses a default stat_ function to plot by default. Sometimes these default calculations make inherent sense. Other times they may not be as obvious. For example, consider this graph:
ggplot(data = gss_sm,
mapping = aes(x = bigregion)) +
geom_bar()
We use a single mapping (aes(x = bigregion)). geom_bar() automatically counts the number of individual observations in the data set for each region of the United States. But where did the y-axis variable count come from? It was calculated by the default stat_count() function. This function actually computes two variables, count and prop (proportion). If we want to draw the chart with relative frequencies, we need to use the prop variable.
ggplot(data = gss_sm,
mapping = aes(x = bigregion)) +
geom_bar(aes(y = ..prop..))
Since count is generated by stat_count() and is not stored anywhere in the R session, it is a temporary variable we access within the ggplot() call using .. notation. This avoids confusion if there was a column called count or prop in gss_sm.
Notice the plot does not look correct. The y-axis is accurate, but all the bars have a value of 1. We want them to instead sum to 1, representing the number of observations per region as a proportion of all respondents in the data set. This is a type of grouping problem. However it is the opposite from before. Rather than grouping by region, we want to explicitly not group by region. To do that, we map a dummy value to the group aesthetic.
ggplot(data = gss_sm,
mapping = aes(x = bigregion,
group = 1)) +
geom_bar(aes(y = ..prop..))
Frequency plots
In drawing frequency plots, we can incorporate color through the fill aesthetic.
ggplot(data = gss_sm,
mapping = aes(x = religion,
fill = religion)) +
geom_bar()
ggplot(data = gss_sm,
mapping = aes(x = religion, fill = religion)) +
geom_bar() +
guides(fill = FALSE) 
Since we map the same variable to two different aesthetics, we don’t really need the legend. We can disable that with guides(fill = FALSE).
The awkward way
More commonly, we would add color to a bar graph to cross-classify two categorical variables. This is the graphical equivalent of a frequency table. We can do this directly within ggplot(), however it is also more convoluted.
Consider examining religious preference by census region.
ggplot(data = gss_sm,
mapping = aes(x = bigregion,
fill = religion)) +
geom_bar()
By default we get a stacked bar chart. If we want to make comparisons easier, we could convert this to a proportional bar chart.
ggplot(data = gss_sm,
mapping = aes(x = bigregion,
fill = religion)) +
geom_bar(position = "fill")
Now all the bars are the same height, but we lost the ability to see the relative size of each region with respect to the overall total. What if we wanted to show the proportion of religions within regions of the country, but instead of stacking the bars we want separate bars? The first attempt may use position = "dodge".
ggplot(data = gss_sm,
mapping = aes(x = bigregion,
fill = religion)) +
geom_bar(position = "dodge")
Good structure, but we’re bach to counts. Let’s directly map the ..prop.. variable to the y aesthetic as well to preserve the proportion on the y-axis.
ggplot(data = gss_sm,
mapping = aes(x = bigregion,
fill = religion)) +
geom_bar(aes(y = ..prop..), position = "dodge")
Still not correct. Same problem as before. Each individual bar sums to 1. If we want overall proportions for a single variable, we mapped group = 1. What if we do that here but with respect to religion?
ggplot(data = gss_sm,
mapping = aes(x = bigregion,
fill = religion)) +
geom_bar(aes(y = ..prop..,
group = religion), position = "dodge")
Looks better, but we still have a problem. Bars within a single region do not sum to 1. Instead, bars for any particular region sum to 1.
ggplot(data = gss_sm,
mapping = aes(x = religion)) +
geom_bar(aes(y = ..prop..,
group = bigregion), position = "dodge") +
facet_wrap(~ bigregion)
The easiest approach is to use facet_wrap() and not force geom_bar() and stat_count() to do all the work in a single step. Instead, we can ask ggplot() to give us a proportional bar chart of religious affiliation, and then facet that by region. The proportions are calculated within each panel, which is the breakdown we wanted. This has the added advantage of not producing too many bars within each category.
Calculate manually
Rather than doing all the summarizing in ggplot(), we could instead calculate the frequencies and proportions manually using dplyr functions first, then use the summarized data frame as the basis for the bar graph.
glimpse(gss_sm)## Observations: 2,867
## Variables: 32
## $ year <dbl> 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 20...
## $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,...
## $ ballot <dbl+lbl> 1, 2, 3, 1, 3, 2, 1, 3, 1, 3, 2, 1, 2, 3, 2, 3...
## $ age <dbl> 47, 61, 72, 43, 55, 53, 50, 23, 45, 71, 33, 86, 32...
## $ childs <dbl> 3, 0, 2, 4, 2, 2, 2, 3, 3, 4, 5, 4, 3, 5, 7, 2, 6,...
## $ sibs <dbl+lbl> 2, 3, 3, 3, 2, 2, 2, 6, 5, 1, 4, 4, 3, 6, 0, 1...
## $ degree <fct> Bachelor, High School, Bachelor, High School, Grad...
## $ race <fct> White, White, White, White, White, White, White, O...
## $ sex <fct> Male, Male, Male, Female, Female, Female, Male, Fe...
## $ region <fct> New England, New England, New England, New England...
## $ income16 <fct> $170000 or over, $50000 to 59999, $75000 to $89999...
## $ relig <fct> None, None, Catholic, Catholic, None, None, None, ...
## $ marital <fct> Married, Never Married, Married, Married, Married,...
## $ padeg <fct> Graduate, Lt High School, High School, NA, Bachelo...
## $ madeg <fct> High School, High School, Lt High School, High Sch...
## $ partyid <fct> Independent, Ind,near Dem, Not Str Republican, Not...
## $ polviews <fct> Moderate, Liberal, Conservative, Moderate, Slightl...
## $ happy <fct> Pretty Happy, Pretty Happy, Very Happy, Pretty Hap...
## $ partners <fct> NA, 1 Partner, 1 Partner, NA, 1 Partner, 1 Partner...
## $ grass <fct> NA, Legal, Not Legal, NA, Legal, Legal, NA, Not Le...
## $ zodiac <fct> Aquarius, Scorpio, Pisces, Cancer, Scorpio, Scorpi...
## $ pres12 <dbl+lbl> 3, 1, 2, 2, 1, 1, NA, NA, NA, 2, NA, NA, 1, 1,...
## $ wtssall <dbl> 0.957, 0.478, 0.957, 1.914, 1.435, 0.957, 1.435, 0...
## $ income_rc <fct> Gt $170000, Gt $50000, Gt $75000, Gt $170000, Gt $...
## $ agegrp <fct> Age 45-55, Age 55-65, Age 65+, Age 35-45, Age 45-5...
## $ ageq <fct> Age 34-49, Age 49-62, Age 62+, Age 34-49, Age 49-6...
## $ siblings <fct> 2, 3, 3, 3, 2, 2, 2, 6+, 5, 1, 4, 4, 3, 6+, 0, 1, ...
## $ kids <fct> 3, 0, 2, 4+, 2, 2, 2, 3, 3, 4+, 4+, 4+, 3, 4+, 4+,...
## $ religion <fct> None, None, Catholic, Catholic, None, None, None, ...
## $ bigregion <fct> Northeast, Northeast, Northeast, Northeast, Northe...
## $ partners_rc <fct> NA, 1, 1, NA, 1, 1, NA, 1, NA, 3, 1, NA, 1, NA, 0,...
## $ obama <dbl> 0, 1, 0, 0, 1, 1, NA, NA, NA, 0, NA, NA, 1, 1, 0, ...(rel_by_region <- gss_sm %>%
group_by(bigregion, religion) %>%
summarize(N = n()) %>%
mutate(freq = N / sum(N),
pct = round((freq * 100), 0)))## # A tibble: 24 x 5
## # Groups: bigregion [4]
## bigregion religion N freq pct
## <fct> <fct> <int> <dbl> <dbl>
## 1 Northeast Protestant 158 0.324 32.
## 2 Northeast Catholic 162 0.332 33.
## 3 Northeast Jewish 27 0.0553 6.
## 4 Northeast None 112 0.230 23.
## 5 Northeast Other 28 0.0574 6.
## 6 Northeast <NA> 1 0.00205 0.
## 7 Midwest Protestant 325 0.468 47.
## 8 Midwest Catholic 172 0.247 25.
## 9 Midwest Jewish 3 0.00432 0.
## 10 Midwest None 157 0.226 23.
## # ... with 14 more rowsNow this is easy to pass into ggplot() and draw the bar graph.
ggplot(data = rel_by_region,
mapping = aes(x = bigregion,
y = pct,
fill = religion)) +
geom_col(position = "dodge2") +
labs(x = "Region", y = "Percent", fill = "Religion") +
theme(legend.position = "top")
Instead of using geom_bar(), we use geom_col() because we already summarized the data - we want stat_identity(), not stat_count(). While this figure works, it is not the best we can do. It is generally crowded. Instead, let’s convert it to a faceted plot:
ggplot(data = rel_by_region,
mapping = aes(x = religion,
y = pct,
fill = religion)) +
geom_col(position = "dodge2") +
labs(x = "Region", y = "Percent", fill = "Religion") +
guides(fill = FALSE) +
coord_flip() +
facet_grid(~ bigregion)
Continuous variables by group or category
glimpse(organdata)## Observations: 238
## Variables: 21
## $ country <chr> "Australia", "Australia", "Australia", "Austr...
## $ year <date> NA, 1991-01-01, 1992-01-01, 1993-01-01, 1994...
## $ donors <dbl> NA, 12.09, 12.35, 12.51, 10.25, 10.18, 10.59,...
## $ pop <int> 17065, 17284, 17495, 17667, 17855, 18072, 183...
## $ pop_dens <dbl> 0.220, 0.223, 0.226, 0.228, 0.231, 0.233, 0.2...
## $ gdp <int> 16774, 17171, 17914, 18883, 19849, 21079, 219...
## $ gdp_lag <int> 16591, 16774, 17171, 17914, 18883, 19849, 210...
## $ health <dbl> 1300, 1379, 1455, 1540, 1626, 1737, 1846, 194...
## $ health_lag <dbl> 1224, 1300, 1379, 1455, 1540, 1626, 1737, 184...
## $ pubhealth <dbl> 4.8, 5.4, 5.4, 5.4, 5.4, 5.5, 5.6, 5.7, 5.9, ...
## $ roads <dbl> 136.6, 122.3, 112.8, 110.5, 108.0, 111.6, 107...
## $ cerebvas <int> 682, 647, 630, 611, 631, 592, 576, 525, 516, ...
## $ assault <int> 21, 19, 17, 18, 17, 16, 17, 17, 16, 15, 16, 1...
## $ external <int> 444, 425, 406, 376, 387, 371, 395, 385, 410, ...
## $ txp_pop <dbl> 0.938, 0.926, 0.915, 0.906, 0.896, 0.885, 0.8...
## $ world <chr> "Liberal", "Liberal", "Liberal", "Liberal", "...
## $ opt <chr> "In", "In", "In", "In", "In", "In", "In", "In...
## $ consent_law <chr> "Informed", "Informed", "Informed", "Informed...
## $ consent_practice <chr> "Informed", "Informed", "Informed", "Informed...
## $ consistent <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Ye...
## $ ccode <chr> "Oz", "Oz", "Oz", "Oz", "Oz", "Oz", "Oz", "Oz...Scatterplot
ggplot(data = organdata,
mapping = aes(x = year, y = donors)) +
geom_point()## Warning: Removed 34 rows containing missing values (geom_point).
Not useful.
Line plots
ggplot(data = organdata,
mapping = aes(x = year, y = donors)) +
geom_line(aes(group = country)) +
facet_wrap(~ country)## Warning: Removed 34 rows containing missing values (geom_path).
Boxplots
ggplot(data = organdata,
mapping = aes(x = country, y = donors)) +
geom_boxplot()## Warning: Removed 34 rows containing non-finite values (stat_boxplot).
Awkward to have country labels on x-axis. Switch to y-axis.
coord_flip()
ggplot(data = organdata,
mapping = aes(x = country, y = donors)) +
geom_boxplot() +
coord_flip()## Warning: Removed 34 rows containing non-finite values (stat_boxplot).
reorder()
ggplot(data = organdata,
mapping = aes(x = reorder(country, donors, na.rm = TRUE),
y = donors)) +
geom_boxplot() +
labs(x = NULL) +
coord_flip()## Warning: Removed 34 rows containing non-finite values (stat_boxplot).
Add color aesthetic.
ggplot(data = organdata,
mapping = aes(x = reorder(country, donors, na.rm = TRUE),
y = donors, fill = world)) +
geom_boxplot() +
labs(x = NULL) +
coord_flip() +
theme(legend.position = "bottom")## Warning: Removed 34 rows containing non-finite values (stat_boxplot).
Strip chart
ggplot(data = organdata,
mapping = aes(x = reorder(country, donors, na.rm = TRUE),
y = donors, color = world)) +
geom_point() +
labs(x = NULL) +
coord_flip() +
theme(legend.position = "bottom")## Warning: Removed 34 rows containing missing values (geom_point).
Hard to see all the points. Add jitter.
ggplot(data = organdata,
mapping = aes(x = reorder(country, donors, na.rm = TRUE),
y = donors, color = world)) +
geom_jitter() +
labs(x = NULL) +
coord_flip() +
theme(legend.position = "bottom")## Warning: Removed 34 rows containing missing values (geom_point).
Cleveland dotplot
Calculate summary statistics
(by_country <- organdata %>%
group_by(consent_law, country) %>%
summarize(donors_mean= mean(donors, na.rm = TRUE),
donors_sd = sd(donors, na.rm = TRUE),
gdp_mean = mean(gdp, na.rm = TRUE),
health_mean = mean(health, na.rm = TRUE),
roads_mean = mean(roads, na.rm = TRUE),
cerebvas_mean = mean(cerebvas, na.rm = TRUE)))## # A tibble: 17 x 8
## # Groups: consent_law [?]
## consent_law country donors_mean donors_sd gdp_mean health_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Informed Australia 10.6 1.14 22179. 1958.
## 2 Informed Canada 14.0 0.751 23711. 2272.
## 3 Informed Denmark 13.1 1.47 23722. 2054.
## 4 Informed Germany 13.0 0.611 22163. 2349.
## 5 Informed Ireland 19.8 2.48 20824. 1480.
## 6 Informed Netherlands 13.7 1.55 23013. 1993.
## 7 Informed United Kingdom 13.5 0.775 21359. 1561.
## 8 Informed United States 20.0 1.33 29212. 3988.
## 9 Presumed Austria 23.5 2.42 23876. 1875.
## 10 Presumed Belgium 21.9 1.94 22500. 1958.
## 11 Presumed Finland 18.4 1.53 21019. 1615.
## 12 Presumed France 16.8 1.60 22603. 2160.
## 13 Presumed Italy 11.1 4.28 21554. 1757.
## 14 Presumed Norway 15.4 1.11 26448. 2217.
## 15 Presumed Spain 28.1 4.96 16933. 1289.
## 16 Presumed Sweden 13.1 1.75 22415. 1951.
## 17 Presumed Switzerland 14.2 1.71 27233. 2776.
## # ... with 2 more variables: roads_mean <dbl>, cerebvas_mean <dbl>Better approach using efficient code.
(by_country <- organdata %>%
group_by(consent_law, country) %>%
summarize_if(is.numeric, funs(mean, sd), na.rm = TRUE) %>%
ungroup())## # A tibble: 17 x 28
## consent_law country donors_mean pop_mean pop_dens_mean gdp_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Informed Australia 10.6 18318. 0.237 22179.
## 2 Informed Canada 14.0 29608. 0.297 23711.
## 3 Informed Denmark 13.1 5257. 12.2 23722.
## 4 Informed Germany 13.0 80255. 22.5 22163.
## 5 Informed Ireland 19.8 3674. 5.23 20824.
## 6 Informed Netherlands 13.7 15548. 37.4 23013.
## 7 Informed United Kingdom 13.5 58187. 24.0 21359.
## 8 Informed United States 20.0 269330. 2.80 29212.
## 9 Presumed Austria 23.5 7927. 9.45 23876.
## 10 Presumed Belgium 21.9 10153. 30.7 22500.
## 11 Presumed Finland 18.4 5112. 1.51 21019.
## 12 Presumed France 16.8 58056. 10.5 22603.
## 13 Presumed Italy 11.1 57360. 19.0 21554.
## 14 Presumed Norway 15.4 4386. 1.35 26448.
## 15 Presumed Spain 28.1 39666. 7.84 16933.
## 16 Presumed Sweden 13.1 8789. 1.95 22415.
## 17 Presumed Switzerland 14.2 7037. 17.0 27233.
## # ... with 22 more variables: gdp_lag_mean <dbl>, health_mean <dbl>,
## # health_lag_mean <dbl>, pubhealth_mean <dbl>, roads_mean <dbl>,
## # cerebvas_mean <dbl>, assault_mean <dbl>, external_mean <dbl>,
## # txp_pop_mean <dbl>, donors_sd <dbl>, pop_sd <dbl>, pop_dens_sd <dbl>,
## # gdp_sd <dbl>, gdp_lag_sd <dbl>, health_sd <dbl>, health_lag_sd <dbl>,
## # pubhealth_sd <dbl>, roads_sd <dbl>, cerebvas_sd <dbl>,
## # assault_sd <dbl>, external_sd <dbl>, txp_pop_sd <dbl>Draw the plot
ggplot(data = by_country,
mapping = aes(x = donors_mean,
y = reorder(country, donors_mean),
color = consent_law)) +
geom_point(size = 3) +
labs(x = "Donor Procurement Rate",
y = "", color = "Consent Law") +
theme(legend.position = "top")
Use facet instead of color
ggplot(data = by_country,
mapping = aes(x = donors_mean,
y = reorder(country, donors_mean))) +
geom_point(size = 3) +
facet_wrap(~ consent_law, ncol = 1) +
labs(x = "Donor Procurement Rate",
y = "", color = "Consent Law")
ggplot(data = by_country,
mapping = aes(x = donors_mean,
y = reorder(country, donors_mean))) +
geom_point(size = 3) +
facet_wrap(~ consent_law, scales = "free_y", ncol = 1) +
labs(x = "Donor Procurement Rate",
y = "", color = "Consent Law")
Add standard deviation
ggplot(data = by_country,
mapping = aes(x = reorder(country, donors_mean),
y = donors_mean)) +
geom_pointrange(mapping = aes(ymin = donors_mean - donors_sd,
ymax = donors_mean + donors_sd)) +
labs(x = "",
y = "Donor Procurement Rate") +
coord_flip()
Plot text directly
geom_text()
ggplot(data = by_country,
mapping = aes(x = roads_mean,
y = donors_mean)) +
geom_point() +
geom_text(mapping = aes(label = country))
ggplot(data = by_country,
mapping = aes(x = roads_mean,
y = donors_mean)) +
geom_point() +
geom_text(mapping = aes(label = country), hjust = 0)
ggrepel::geom_text_repel()
elections_historic %>%
select(2:7)## # A tibble: 49 x 6
## year winner win_party ec_pct popular_pct popular_margin
## <int> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1824 John Quincy Adams D.-R. 0.322 0.309 -0.104
## 2 1828 Andrew Jackson Dem. 0.682 0.559 0.122
## 3 1832 Andrew Jackson Dem. 0.766 0.547 0.178
## 4 1836 Martin Van Buren Dem. 0.578 0.508 0.142
## 5 1840 William Henry Harris… Whig 0.796 0.529 0.0605
## 6 1844 James Polk Dem. 0.618 0.495 0.0145
## 7 1848 Zachary Taylor Whig 0.562 0.473 0.0479
## 8 1852 Franklin Pierce Dem. 0.858 0.508 0.0695
## 9 1856 James Buchanan Dem. 0.588 0.453 0.122
## 10 1860 Abraham Lincoln Rep. 0.594 0.396 0.101
## # ... with 39 more rowsp_title <- "Presidential Elections: Popular & Electoral College Margins"
p_subtitle <- "1824-2016"
p_caption <- "Data for 2016 are provisional."
x_label <- "Winner's share of Popular Vote"
y_label <- "Winner's share of Electoral College Votes"
library(ggrepel)
ggplot(elections_historic, aes(x = popular_pct,
y = ec_pct,
label = winner_label)) +
geom_hline(yintercept = 0.5, size = 1.4, color = "gray80") +
geom_vline(xintercept = 0.5, size = 1.4, color = "gray80") +
geom_point() +
geom_text_repel() +
scale_x_continuous(labels = scales::percent) +
scale_y_continuous(labels = scales::percent) +
labs(x = x_label, y = y_label, title = p_title, subtitle = p_subtitle,
caption = p_caption)
Label outliers only
ggplot(data = by_country,
mapping = aes(x = gdp_mean, y = health_mean)) +
geom_point() +
geom_text_repel(data = filter(by_country, gdp_mean > 25000),
mapping = aes(label = country))
ggplot(data = by_country,
mapping = aes(x = gdp_mean, y = health_mean)) +
geom_point() +
geom_text_repel(data = filter(by_country,
gdp_mean > 25000 | health_mean < 1500 |
country %in% "Belgium"),
mapping = aes(label = country))
Session Info
devtools::session_info()## Session info -------------------------------------------------------------## setting value
## version R version 3.4.3 (2017-11-30)
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/Chicago
## date 2018-04-10## Packages -----------------------------------------------------------------## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.0)
## backports 1.1.2 2017-12-13 CRAN (R 3.4.3)
## base * 3.4.3 2017-12-07 local
## bindr 0.1.1 2018-03-13 CRAN (R 3.4.3)
## bindrcpp * 0.2.2.9000 2018-04-08 Github (krlmlr/bindrcpp@bd5ae73)
## broom * 0.4.4 2018-03-29 CRAN (R 3.4.3)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.4.0)
## cli 1.0.0 2017-11-05 CRAN (R 3.4.2)
## codetools 0.2-15 2016-10-05 CRAN (R 3.4.3)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
## compiler 3.4.3 2017-12-07 local
## crayon 1.3.4 2017-10-03 Github (gaborcsardi/crayon@b5221ab)
## datasets * 3.4.3 2017-12-07 local
## devtools 1.13.5 2018-02-18 CRAN (R 3.4.3)
## digest 0.6.15 2018-01-28 CRAN (R 3.4.3)
## dplyr * 0.7.4.9003 2018-04-08 Github (tidyverse/dplyr@b7aaa95)
## evaluate 0.10.1 2017-06-24 CRAN (R 3.4.1)
## forcats * 0.3.0 2018-02-19 CRAN (R 3.4.3)
## foreign 0.8-69 2017-06-22 CRAN (R 3.4.3)
## gapminder * 0.3.0 2017-10-31 CRAN (R 3.4.2)
## ggplot2 * 2.2.1.9000 2018-04-10 Github (tidyverse/ggplot2@3c9c504)
## ggrepel * 0.7.0 2017-09-29 CRAN (R 3.4.2)
## glue 1.2.0 2017-10-29 CRAN (R 3.4.2)
## graphics * 3.4.3 2017-12-07 local
## grDevices * 3.4.3 2017-12-07 local
## grid 3.4.3 2017-12-07 local
## gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
## haven 1.1.1 2018-01-18 CRAN (R 3.4.3)
## hms 0.4.2 2018-03-10 CRAN (R 3.4.3)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.4.0)
## httr 1.3.1 2017-08-20 CRAN (R 3.4.1)
## jsonlite 1.5 2017-06-01 CRAN (R 3.4.0)
## knitr * 1.20 2018-02-20 CRAN (R 3.4.3)
## labeling 0.3 2014-08-23 CRAN (R 3.4.0)
## lattice 0.20-35 2017-03-25 CRAN (R 3.4.3)
## lazyeval 0.2.1 2017-10-29 CRAN (R 3.4.2)
## lubridate 1.7.3 2018-02-27 CRAN (R 3.4.3)
## magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
## Matrix 1.2-12 2017-11-20 CRAN (R 3.4.3)
## memoise 1.1.0 2017-04-21 CRAN (R 3.4.0)
## methods * 3.4.3 2017-12-07 local
## mgcv 1.8-23 2018-01-15 CRAN (R 3.4.3)
## mnormt 1.5-5 2016-10-15 CRAN (R 3.4.0)
## modelr 0.1.1 2017-08-10 local
## munsell 0.4.3 2016-02-13 CRAN (R 3.4.0)
## nlme 3.1-131.1 2018-02-16 CRAN (R 3.4.3)
## parallel 3.4.3 2017-12-07 local
## pillar 1.2.1 2018-02-27 CRAN (R 3.4.3)
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.0)
## plyr 1.8.4 2016-06-08 CRAN (R 3.4.0)
## psych 1.7.8 2017-09-09 CRAN (R 3.4.1)
## purrr * 0.2.4 2017-10-18 CRAN (R 3.4.2)
## R6 2.2.2 2017-06-17 CRAN (R 3.4.0)
## Rcpp 0.12.16 2018-03-13 CRAN (R 3.4.4)
## readr * 1.1.1 2017-05-16 CRAN (R 3.4.0)
## readxl 1.0.0 2017-04-18 CRAN (R 3.4.0)
## reshape2 1.4.3 2017-12-11 CRAN (R 3.4.3)
## rlang 0.2.0.9001 2018-04-10 Github (r-lib/rlang@70d2d40)
## rmarkdown 1.9 2018-03-01 CRAN (R 3.4.3)
## rprojroot 1.3-2 2018-01-03 CRAN (R 3.4.3)
## rstudioapi 0.7 2017-09-07 CRAN (R 3.4.1)
## rvest 0.3.2 2016-06-17 CRAN (R 3.4.0)
## scales 0.5.0.9000 2018-04-10 Github (hadley/scales@d767915)
## socviz * 0.3.0 2018-04-08 Github (kjhealy/socviz@a0e00ac)
## stats * 3.4.3 2017-12-07 local
## stringi 1.1.7 2018-03-12 CRAN (R 3.4.3)
## stringr * 1.3.0 2018-02-19 CRAN (R 3.4.3)
## tibble * 1.4.2 2018-01-22 CRAN (R 3.4.3)
## tidyr * 0.8.0 2018-01-29 CRAN (R 3.4.3)
## tidyselect 0.2.4 2018-02-26 CRAN (R 3.4.3)
## tidyverse * 1.2.1 2017-11-14 CRAN (R 3.4.2)
## tools 3.4.3 2017-12-07 local
## utf8 1.1.3 2018-01-03 CRAN (R 3.4.3)
## utils * 3.4.3 2017-12-07 local
## withr 2.1.2 2018-04-10 Github (jimhester/withr@79d7b0d)
## xml2 1.2.0 2018-01-24 CRAN (R 3.4.3)
## yaml 2.1.18 2018-03-08 CRAN (R 3.4.4)